wallstreet_feat_eng_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\morga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\morga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\morga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\morga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import re\n",
    "from src import utils, nlp_utils\n",
    "from bs4 import BeautifulSoup\n",
    "import itertools\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "nltk.download('wordnet')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "import string\n",
    "from datetime import datetime\n",
    "from collections import Counter\n",
    "\n",
    "from src.process_text_variables import contracted_words_dict, stop_words_dict, punc \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wall Street Forum\n",
    "\n",
    "## EDA - Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wallstreet = pd.read_csv('data/wallstreet.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Postings:  45372\n",
      "# of Unique Threads :  8755\n",
      "# of Unique Subforums :  40\n",
      "# of Unique Authors :  5533\n",
      "# of Membership Types :  8\n"
     ]
    }
   ],
   "source": [
    "total_num_posts_wall = wallstreet.shape[0] \n",
    "print('# of Postings: ', total_num_posts_wall)\n",
    "print('# of Unique Threads : ', len(wallstreet['threadID'].unique()))\n",
    "print('# of Unique Subforums : ', len(wallstreet['subforum'].unique()))\n",
    "print('# of Unique Authors : ', len(wallstreet['authorName'].unique()))\n",
    "print('# of Membership Types : ', len(wallstreet['postAuthorMembership'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>num_postings</th>\n",
       "      <th>prcnt_total_posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>med3l1n</td>\n",
       "      <td>4889</td>\n",
       "      <td>0.107754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6LACK</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.033060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>leaguemode</td>\n",
       "      <td>1146</td>\n",
       "      <td>0.025258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ExpectUS</td>\n",
       "      <td>1113</td>\n",
       "      <td>0.024531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PurityRing</td>\n",
       "      <td>1111</td>\n",
       "      <td>0.024486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BonesKoopa</td>\n",
       "      <td>476</td>\n",
       "      <td>0.010491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Icepapi</td>\n",
       "      <td>446</td>\n",
       "      <td>0.009830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>code48k</td>\n",
       "      <td>379</td>\n",
       "      <td>0.008353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>deusxmacina</td>\n",
       "      <td>354</td>\n",
       "      <td>0.007802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dabbingtimes</td>\n",
       "      <td>334</td>\n",
       "      <td>0.007361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         author  num_postings  prcnt_total_posts\n",
       "0       med3l1n          4889           0.107754\n",
       "1         6LACK          1500           0.033060\n",
       "2    leaguemode          1146           0.025258\n",
       "3      ExpectUS          1113           0.024531\n",
       "4    PurityRing          1111           0.024486\n",
       "5    BonesKoopa           476           0.010491\n",
       "6       Icepapi           446           0.009830\n",
       "7       code48k           379           0.008353\n",
       "8   deusxmacina           354           0.007802\n",
       "9  dabbingtimes           334           0.007361"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall_authors = pd.DataFrame(wallstreet['authorName'].value_counts()).reset_index()\n",
    "wall_authors.columns = ['author','num_postings']\n",
    "wall_authors['prcnt_total_posts'] = wall_authors['num_postings'] / total_num_posts_wall\n",
    "wall_authors.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thread_topic</th>\n",
       "      <th>num_postings</th>\n",
       "      <th>prcnt_total_posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GozNym 2.0 Banking Bot (Page 1 of 3)</td>\n",
       "      <td>75</td>\n",
       "      <td>0.001653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GozNym 2.0 Banking Bot (Page 2 of 3)</td>\n",
       "      <td>75</td>\n",
       "      <td>0.001653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Price &amp; Version update] BTC ATM Malware</td>\n",
       "      <td>73</td>\n",
       "      <td>0.001609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GozNym 2.0 Banking Bot (Page 3 of 3)</td>\n",
       "      <td>56</td>\n",
       "      <td>0.001234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hello</td>\n",
       "      <td>52</td>\n",
       "      <td>0.001146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CASHAPP GUIDE</td>\n",
       "      <td>50</td>\n",
       "      <td>0.001102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANZ AU Bank accounts with security Q&amp;A! (Page ...</td>\n",
       "      <td>50</td>\n",
       "      <td>0.001102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ANZ AU Bank accounts with security Q&amp;A! (Page ...</td>\n",
       "      <td>50</td>\n",
       "      <td>0.001102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>US Tax Refund: 2018 Tax W2 Forms With AGI &amp; DO...</td>\n",
       "      <td>50</td>\n",
       "      <td>0.001102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US Tax Refund: 2018 Tax W2 Forms With AGI &amp; DO...</td>\n",
       "      <td>50</td>\n",
       "      <td>0.001102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                        thread_topic  num_postings  \\\n",
       "0               GozNym 2.0 Banking Bot (Page 1 of 3)            75   \n",
       "1               GozNym 2.0 Banking Bot (Page 2 of 3)            75   \n",
       "2           [Price & Version update] BTC ATM Malware            73   \n",
       "3               GozNym 2.0 Banking Bot (Page 3 of 3)            56   \n",
       "4                                              Hello            52   \n",
       "5                                      CASHAPP GUIDE            50   \n",
       "6  ANZ AU Bank accounts with security Q&A! (Page ...            50   \n",
       "7  ANZ AU Bank accounts with security Q&A! (Page ...            50   \n",
       "8  US Tax Refund: 2018 Tax W2 Forms With AGI & DO...            50   \n",
       "9  US Tax Refund: 2018 Tax W2 Forms With AGI & DO...            50   \n",
       "\n",
       "   prcnt_total_posts  \n",
       "0           0.001653  \n",
       "1           0.001653  \n",
       "2           0.001609  \n",
       "3           0.001234  \n",
       "4           0.001146  \n",
       "5           0.001102  \n",
       "6           0.001102  \n",
       "7           0.001102  \n",
       "8           0.001102  \n",
       "9           0.001102  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall_threads = pd.DataFrame(wallstreet['threadTitle'].value_counts()).reset_index()\n",
    "wall_threads.columns = ['thread_topic','num_postings']\n",
    "wall_threads['prcnt_total_posts'] = wall_threads['num_postings'] / total_num_posts_wall\n",
    "wall_threads.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "wall_subforums = pd.DataFrame(wallstreet['subforum'].value_counts()).reset_index()\n",
    "wall_subforums.columns = ['subforum','num_postings']\n",
    "wall_subforums['prcnt_total_posts'] = wall_subforums['num_postings'] / total_num_posts_wall\n",
    "wall_subforums.head(10)\n",
    "\n",
    "wall_subforums.to_csv('img/top10_subforums.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Subforums:  40\n",
      "['Fraud' 'General Questions' 'Carding Stuff' 'Services' 'Cannabis'\n",
      " 'Introductions' 'Counterfeits' 'Stimulants' 'Announcements'\n",
      " 'Digital goods' 'Guides & Tutorials' 'Off-Topic'\n",
      " 'Wanted - Carding, Fraud & Related' 'Opiates' 'Bitcoin & Multisig'\n",
      " 'Feature Request' 'Software & Malware' 'Bug Reports' 'Scamwatch'\n",
      " 'Psychedelics' 'MDMA' 'Ecstasy' 'Benzos' 'Pharmaceuticals'\n",
      " 'Security & Hosting' 'LE Watch & Arrest Reports' 'Dissociatives'\n",
      " 'Wanted - Drugs & Related' 'Sports' 'Jewelry & Gold' 'Opsec' 'Finances'\n",
      " 'Steroids' 'Ebooks' 'Funny & Jokes' 'Reading club' 'Research Chemicals'\n",
      " 'Hardware' 'Drug safety' 'Kratom']\n"
     ]
    }
   ],
   "source": [
    "unique_subs = wall_subforums['subforum'].unique()\n",
    "print('# of Subforums: ', len(unique_subs))\n",
    "print(unique_subs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud= ['Fraud', 'Carding Stuff', 'Scamwatch', 'Counterfeits', 'Wanted - Carding, Fraud & Related',]\n",
    "general_admin = [ 'General Questions', 'Introductions', 'Announcements', 'Feature Request']\n",
    "hacking_security = ['Software & Malware', 'Bug Reports', 'Scamwatch', 'Security & Hosting', 'Opsec']\n",
    "drugs = ['Cannabis', 'Counterfeits', 'Stimulants', 'Psychedelics', 'MDMA', 'Ecstasy', 'Benzos', 'Dissociatives', 'Wanted - Drugs & Related',  'Steroids', 'Research Chemicals', 'Drug safety', 'Opiates', 'Kratom']\n",
    "education = ['Guides & Tutorials',  'Reading club', 'Ebooks']\n",
    "crypto_finance = ['Bitcoin & Multisig', 'Finances']\n",
    "other = ['Sports', 'Jewelry & Gold', 'LE Watch & Arrest Reports', 'Funny & Jokes', 'Jewelry & Gold',  'Off-Topic', 'Hardware']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_subforum(sub):\n",
    "    if sub in fraud:\n",
    "        group = 'Fraud'\n",
    "    elif sub in general_admin:\n",
    "        group = 'General & Admin'\n",
    "    elif sub in hacking_security:\n",
    "        group = 'Hacking & Security'\n",
    "    elif sub in drugs:\n",
    "        group = 'Drugs'\n",
    "    elif sub in education:\n",
    "        group = 'Education'\n",
    "    elif sub in crypto_finance:\n",
    "        group = 'Cryptocurrency & Finance'\n",
    "    else:\n",
    "        group = 'Other'\n",
    "    return group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>num_postings</th>\n",
       "      <th>prcnt_total_posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cryptocurrency &amp; Finance</td>\n",
       "      <td>728</td>\n",
       "      <td>0.016045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drugs</td>\n",
       "      <td>6896</td>\n",
       "      <td>0.151988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Education</td>\n",
       "      <td>1295</td>\n",
       "      <td>0.028542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fraud</td>\n",
       "      <td>19143</td>\n",
       "      <td>0.421912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>General &amp; Admin</td>\n",
       "      <td>9804</td>\n",
       "      <td>0.216080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hacking &amp; Security</td>\n",
       "      <td>1499</td>\n",
       "      <td>0.033038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Other</td>\n",
       "      <td>6007</td>\n",
       "      <td>0.132394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   category  num_postings  prcnt_total_posts\n",
       "0  Cryptocurrency & Finance           728           0.016045\n",
       "1                     Drugs          6896           0.151988\n",
       "2                 Education          1295           0.028542\n",
       "3                     Fraud         19143           0.421912\n",
       "4           General & Admin          9804           0.216080\n",
       "5        Hacking & Security          1499           0.033038\n",
       "6                     Other          6007           0.132394"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall_subforums['category'] = wall_subforums['subforum'].apply(categorize_subforum)\n",
    "subforums_category_grpd = wall_subforums.groupby('category').agg(sum)\n",
    "\n",
    "categories_grpd = pd.DataFrame(subforums_category_grpd).reset_index()\n",
    "categories_grpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Confirm that it equals 0.\n",
    "sum(categories_grpd['num_postings']) - len(wallstreet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g, ax = plt.subplots(figsize=(11,6))\n",
    "\n",
    "ax = sns.barplot(data=categories_grpd, x='category', y='num_postings')\n",
    "ax.set_title('Forum Posting Distribution by Created Categories', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('Category', fontsize=14)\n",
    "ax.set_ylabel('Number of Postings')\n",
    "\n",
    "plt.tight_layout();\n",
    "plt.savefig('img/posting_distribution.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>join_date</th>\n",
       "      <th>count</th>\n",
       "      <th>prcnt_total_posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Despacitoland</td>\n",
       "      <td>4889</td>\n",
       "      <td>0.107754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.033060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>1227</td>\n",
       "      <td>0.027043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>1146</td>\n",
       "      <td>0.025258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>InterWebz</td>\n",
       "      <td>1113</td>\n",
       "      <td>0.024531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>524</td>\n",
       "      <td>0.011549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>498</td>\n",
       "      <td>0.010976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>427</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>415</td>\n",
       "      <td>0.009147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>403</td>\n",
       "      <td>0.008882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       join_date  count  prcnt_total_posts\n",
       "0  Despacitoland   4889           0.107754\n",
       "1              G   1500           0.033060\n",
       "2     2017-11-06   1227           0.027043\n",
       "3     2018-01-10   1146           0.025258\n",
       "4      InterWebz   1113           0.024531\n",
       "5     2017-07-27    524           0.011549\n",
       "6     2018-03-03    498           0.010976\n",
       "7     2017-11-09    427           0.009411\n",
       "8     2018-05-06    415           0.009147\n",
       "9     2017-10-17    403           0.008882"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall_joindate = pd.DataFrame(wallstreet['postAuthorJoinDate'].value_counts()).reset_index()\n",
    "wall_joindate.columns = ['join_date','count']\n",
    "wall_joindate['prcnt_total_posts'] = wall_joindate['count'] / total_num_posts_wall\n",
    "wall_joindate.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Max value ~ 5,400.\n",
    "constraint = 500\n",
    "reputation = wallstreet[wallstreet['authorReputation'] < constraint]['authorReputation']\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.distplot(reputation)\n",
    "ax.set_title(\"Author's Reputation Score Distribution\", fontsize=14);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/distributions.py:288: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "constraint = 100\n",
    "likes = wallstreet[wallstreet['likes'] < constraint]['likes']\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax = sns.distplot(likes)\n",
    "ax.set_title(\"A Post's Like Distribution\", fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Data Processing / Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "wallstreet_v2 = wallstreet.copy()\n",
    "\n",
    "# Date/time-related features\n",
    "wallstreet_v2['post_date_time'] = wallstreet_v2['postDate'].apply(utils.fix_postdate_col)\n",
    "wallstreet_v2['author_join_date'] = wallstreet_v2.apply(lambda x: utils.fix_author_join_date_col(x['postAuthorJoinDate'], x['postAuthorMembership'], x['post_date_time']), axis=1)\n",
    "wallstreet_v2['post_date'] = wallstreet_v2['post_date_time'].apply(lambda x: x.date())\n",
    "wallstreet_v2['num_days_member_when_posted'] = wallstreet_v2['post_date_time'].dt.date - wallstreet_v2['author_join_date'].dt.date\n",
    "wallstreet_v2['num_days_member_when_posted'] = wallstreet_v2['num_days_member_when_posted'].dt.days\n",
    "\n",
    "cols_to_drop = ['URL', 'likes', 'postDate', 'postAuthorJoinDate', 'flatContent']\n",
    "wallstreet_v2.drop(cols_to_drop, axis=1, inplace=True)\n",
    "\n",
    "# Text-related features\n",
    "wallstreet_v2['processed_text'] = wallstreet_v2['contentWithHTMLTag'].apply(lambda x: nlp_utils.process_text(x, contracted_words_dict, punc, stop_words_dict, min_len=2))\n",
    "wallstreet_v2['parts_of_speech'] = wallstreet_v2['processed_text'].apply(lambda x: nltk.pos_tag(x))\n",
    "wallstreet_v2['cleaned_post'] = wallstreet_v2['processed_text'].apply(lambda x: ' '.join(x)).astype('str')\n",
    "\n",
    "# VADER Sentiment Analysis\n",
    "si_analyzer = SentimentIntensityAnalyzer()\n",
    "wallstreet_v2['compound_sentiment'] = wallstreet_v2['cleaned_post'].apply(lambda x: si_analyzer.polarity_scores(x)['compound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>postID</th>\n",
       "      <th>threadID</th>\n",
       "      <th>threadTitle</th>\n",
       "      <th>subforum</th>\n",
       "      <th>authorName</th>\n",
       "      <th>postAuthorMembership</th>\n",
       "      <th>authorReputation</th>\n",
       "      <th>postSequence</th>\n",
       "      <th>contentWithHTMLTag</th>\n",
       "      <th>post_date_time</th>\n",
       "      <th>author_join_date</th>\n",
       "      <th>post_date</th>\n",
       "      <th>num_days_member_when_posted</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>parts_of_speech</th>\n",
       "      <th>cleaned_post</th>\n",
       "      <th>compound_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Hi</td>\n",
       "      <td>Introductions</td>\n",
       "      <td>Punka421</td>\n",
       "      <td>New member</td>\n",
       "      <td>-3</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;div class=\"entry-content\"&gt;\\n&lt;p&gt;Just thought I...</td>\n",
       "      <td>2016-10-26 13:58:36</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[thought, introduce, new, community, trying, l...</td>\n",
       "      <td>[(thought, VBN), (introduce, VB), (new, JJ), (...</td>\n",
       "      <td>thought introduce new community trying learn m...</td>\n",
       "      <td>0.9287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Hi</td>\n",
       "      <td>Introductions</td>\n",
       "      <td>WSM</td>\n",
       "      <td>Administrator</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;div class=\"entry-content\"&gt;\\n&lt;p&gt;Hello &lt;img alt...</td>\n",
       "      <td>2016-10-26 14:04:04</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>24.0</td>\n",
       "      <td>[hello, nice, see, regard]</td>\n",
       "      <td>[(hello, JJ), (nice, JJ), (see, NN), (regard, ...</td>\n",
       "      <td>hello nice see regard</td>\n",
       "      <td>0.4215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>WSM Updates - Changelog (Page 1 of 4)</td>\n",
       "      <td>Announcements</td>\n",
       "      <td>WSM</td>\n",
       "      <td>Administrator</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;div class=\"entry-content\"&gt;\\n&lt;p&gt;Hello everyone...</td>\n",
       "      <td>2016-10-26 16:54:27</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>24.0</td>\n",
       "      <td>[hello, everyone, like, tell, implemented, new...</td>\n",
       "      <td>[(hello, RB), (everyone, NN), (like, IN), (tel...</td>\n",
       "      <td>hello everyone like tell implemented new featu...</td>\n",
       "      <td>0.9825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Hi</td>\n",
       "      <td>Introductions</td>\n",
       "      <td>Estrazy</td>\n",
       "      <td>Banned</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>&lt;div class=\"entry-content\"&gt;\\n&lt;p&gt;Hello Punka! n...</td>\n",
       "      <td>2016-10-27 14:00:16</td>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[hello, punka, nice, meet, asked, stay, secure...</td>\n",
       "      <td>[(hello, NN), (punka, NN), (nice, JJ), (meet, ...</td>\n",
       "      <td>hello punka nice meet asked stay secure tip ca...</td>\n",
       "      <td>0.9022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>WSM Updates - Changelog (Page 1 of 4)</td>\n",
       "      <td>Announcements</td>\n",
       "      <td>WSM</td>\n",
       "      <td>Administrator</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;div class=\"entry-content\"&gt;\\n&lt;h5&gt;Changelog fro...</td>\n",
       "      <td>2016-11-02 15:42:27</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>31.0</td>\n",
       "      <td>[changelog, wednesday, november, lot, small, i...</td>\n",
       "      <td>[(changelog, NN), (wednesday, JJ), (november, ...</td>\n",
       "      <td>changelog wednesday november lot small issue b...</td>\n",
       "      <td>0.8720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   postID  threadID                            threadTitle       subforum  \\\n",
       "0       6         5                                     Hi  Introductions   \n",
       "1       7         5                                     Hi  Introductions   \n",
       "2       8         6  WSM Updates - Changelog (Page 1 of 4)  Announcements   \n",
       "3      11         5                                     Hi  Introductions   \n",
       "4      13         6  WSM Updates - Changelog (Page 1 of 4)  Announcements   \n",
       "\n",
       "  authorName postAuthorMembership  authorReputation  postSequence  \\\n",
       "0   Punka421           New member                -3             1   \n",
       "1        WSM        Administrator                66             2   \n",
       "2        WSM        Administrator                74             1   \n",
       "3    Estrazy               Banned                 0             3   \n",
       "4        WSM        Administrator                74             2   \n",
       "\n",
       "                                  contentWithHTMLTag      post_date_time  \\\n",
       "0  <div class=\"entry-content\">\\n<p>Just thought I... 2016-10-26 13:58:36   \n",
       "1  <div class=\"entry-content\">\\n<p>Hello <img alt... 2016-10-26 14:04:04   \n",
       "2  <div class=\"entry-content\">\\n<p>Hello everyone... 2016-10-26 16:54:27   \n",
       "3  <div class=\"entry-content\">\\n<p>Hello Punka! n... 2016-10-27 14:00:16   \n",
       "4  <div class=\"entry-content\">\\n<h5>Changelog fro... 2016-11-02 15:42:27   \n",
       "\n",
       "  author_join_date   post_date  num_days_member_when_posted  \\\n",
       "0       2016-10-26  2016-10-26                          0.0   \n",
       "1       2016-10-02  2016-10-26                         24.0   \n",
       "2       2016-10-02  2016-10-26                         24.0   \n",
       "3       2016-10-27  2016-10-27                          0.0   \n",
       "4       2016-10-02  2016-11-02                         31.0   \n",
       "\n",
       "                                      processed_text  \\\n",
       "0  [thought, introduce, new, community, trying, l...   \n",
       "1                         [hello, nice, see, regard]   \n",
       "2  [hello, everyone, like, tell, implemented, new...   \n",
       "3  [hello, punka, nice, meet, asked, stay, secure...   \n",
       "4  [changelog, wednesday, november, lot, small, i...   \n",
       "\n",
       "                                     parts_of_speech  \\\n",
       "0  [(thought, VBN), (introduce, VB), (new, JJ), (...   \n",
       "1  [(hello, JJ), (nice, JJ), (see, NN), (regard, ...   \n",
       "2  [(hello, RB), (everyone, NN), (like, IN), (tel...   \n",
       "3  [(hello, NN), (punka, NN), (nice, JJ), (meet, ...   \n",
       "4  [(changelog, NN), (wednesday, JJ), (november, ...   \n",
       "\n",
       "                                        cleaned_post  compound_sentiment  \n",
       "0  thought introduce new community trying learn m...              0.9287  \n",
       "1                              hello nice see regard              0.4215  \n",
       "2  hello everyone like tell implemented new featu...              0.9825  \n",
       "3  hello punka nice meet asked stay secure tip ca...              0.9022  \n",
       "4  changelog wednesday november lot small issue b...              0.8720  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wallstreet_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "wallstreet_v2.to_csv('data/wallstreet_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# EDA using Engineering Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wallstreet_import = pd.read_csv('data/wallstreet_master.csv', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Compound Sentiment Score Distribution')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T/La8L7VmeWHj/V08npjq8+f9st4e9K5M0kd19CEASmN5nPLG6/mJvA/IRkmvyvuQvEWxDQFQKu/L7x/yxodp9h/LWn+5JQH3GSHviLqN8gakOyjpTXmHiM88hsdysrygVq8eDCYZsOzAx64uDsn2X5ff8B9Do7wv1TJ54+Z0HF7gHdurw23PqtPh2/K+EJ+U1wG52yEZ5LUu/MTfhhXy3h8H5Y1L9QVJ6QH3OVnSA/LCfbO81+2deudAmJ+QF2ib/HqeknRmwPKOOmxBrM/VUe7f/ppov7T4tb8i78ir2V3c74htKq/j8u/9ddf6r5kyeUdm5ne67xJ5obNJXQwmGcs6O06TF0Zvl/dDr0HeUWJBQzXk+M9N+3wvyDsqLWjZ2fLGHquQF5AOv9aC5venz/G3f7Xe/mzpcjDJLh5nj4aq4JKcC+duQ1KZ2Ycl/VnS2c65Z5NbDQAAb6NPEgAAQABCEgAAQABCEgAAQAD6JAEAAASgJQkAACBAXMZJGjFihCstLY3HogEAAPrUypUr9znn3jEob1xCUmlpqVasWBGPRQMAAPQpM9seNJ3dbQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAEISQAAAAHCyS4AACTpzuU7up3n/aeMT0AlAOChJQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACAAIQkAACBAOJaZzGybpDpJEUltzrkF8SwKAAAg2WIKSb6znXP74lYJAABAP8LuNgAAgACxhiQn6UkzW2lm18azIAAAgP4g1t1tC51ze8ysWNJTZrbeObes4wx+eLpWksaPH9/HZQIAACRWTC1Jzrk9/nWlpPslnRwwz03OuQXOuQVFRUV9WyUAAECCdRuSzCzbzHLb/5Z0vqQ18S4MAAAgmWLZ3TZS0v1m1j7/nc65x+NaFQAAQJJ1G5Kcc1skzUlALQAAAP0GQwAAAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEICQBAAAEiDkkmVmKma0ys0fiWRAAAEB/0JOWpM9JKotXIQAAAP1JTCHJzMZKerekP8S3HAAAgP4h1pakn0u6QVK0qxnM7FozW2FmK6qqqvqkOAAAgGTpNiSZ2cWSKp1zK482n3PuJufcAufcgqKioj4rEAAAIBliaUlaKOlSM9sm6S+SzjGz/4trVQAAAEnWbUhyzv2Hc26sc65U0nslPeOc+7e4VwYAAJBEjJMEAAAQINyTmZ1zz0p6Ni6VAAAA9CO0JAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAQgJAEAAAToNiSZWYaZvWJmq81srZl9OxGFAQAAJFM4hnmaJZ3jnDtkZqmSXjCzvzvnXo5zbQAAAEnTbUhyzjlJh/x/U/2Li2dRAAAAyRZTnyQzSzGz1yVVSnrKObc8vmUBAAAkV0whyTkXcc7NlTRW0slmdnzneczsWjNbYWYrqqqq+rpOAACAhOrR0W3OuYOSnpV0YcBtNznnFjjnFhQVFfVReQAAAMkRy9FtRWZW4P+dKWmxpPXxLgwAACCZYjm6bZSkW80sRV6ousc590h8ywIAAEiuWI5ue0PSvATUAgAA0G8w4jYAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAECAbkOSmY0zs3+YWZmZrTWzzyWiMAAAgGQKxzBPm6QvOudeM7NcSSvN7Cnn3Lo41wYAAJA03bYkOef2Oude8/+uk1QmaUy8CwMAAEimHvVJMrNSSfMkLQ+47VozW2FmK6qqqvqmOgAAgCSJOSSZWY6k+yRd75yr7Xy7c+4m59wC59yCoqKivqwRAAAg4WIKSWaWKi8g3eGc+1t8SwIAAEi+WI5uM0l/lFTmnPtp/EsCAABIvlhakhZK+oCkc8zsdf+yJM51AQAAJFW3QwA4516QZAmoBQAAoN9gxG0AAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAAhCQAAIAA4WQXAABdaWyJ6PG15crLDGv8sCzVNbUqNyM12WUBGCIISQD6Jeec/rZql9btqfX+l3TLS9s0a3Sefv6vczW5ODe5BQIY9AhJAPqll7dWa+2eWl10fIlOKh2mndUNys9K1Z9f3KavP7BGd33iVJlZsssEMIjRJwlAv7PnYKMee3Ovpo3M1cLJI5SRmqIpI3N1/eKp+tL5U/Xylmo9sbY82WUCGOQISQD6lebWiO56ZYey01J01fyxCnVqLXrfyeM1bWSuvvNomZpaI0mqEsBQQEgC0G8453T/67tVXd+ifz1pvLLT39kjIJwS0jcvmaldBxr1xxe2JqFKAEMFIQlAv7Ftf4Pe2FWjc2cUa+KI7C7nWzh5hC6YNVK/+ccmVdQ2JbBCAEMJIQlAv7FiW7XSwyGdMbmo23n/c8lMtUWcfvD4+gRUBmAoIiQB6BeaWiNas6dGs8cWKC3c/UfT+OFZ+tiZE/W313Zrze6aBFQIYKghJAHoF97YVaPWiNOCCYUx3+dTi45Tejike1bsjGNlAIYqQhKAfmHF9moV56ZrbGFmzPfJy0jV4pkj9cgbe9UaicaxOgBDESEJQNJtKK/TrgONWlA6rMcDRF42Z7Sq61v0wqZ9caoOwFDFiNsAku6eFTuVYqa54wp6fN9F04qVn5mqB1ft1tnTiuNQHTBw3bl8R7fzvP+U8QmoZGCiJQlAUrW0RXX/qt2aPipXOQHjInUnLRzSkhNG6cl1FWpoaYtDhQCGKkISgKRaWlah6voWLZgwrNfLuHzuaDW0RPTUuoo+rAzAUEdIApBU96zYqZK8DE0ZmdPrZZxUOkyj8zP04Ot7+rAyAEMdIQlA0lTUNmnZxqrAc7T1RChkumTuaC3bWKXq+pY+rBDAUEZIApA0T62rUNRJl84dfczLunzuGLVFnR59g9YkAH2DkAQgaZ4uq9D4YVmaUtz7XW3tppfkaurIHHa5AegzhCQASVHf3KYXN+/X4hkjezw2UhAz02Vzx2jF9gPaWd3QBxUCGOoISQCS4vm39qmlLarFM/tubKNL53i77Z5YW95nywQwdBGSACTF02UVys0I66TS3h/639m4YVmaXpLLUAAA+gQhCUDCRaJOz6yv1NnTipWa0rcfQ4tnjNSK7Qd0sIGj3AAcG0ISgIR7fecB7a9v0eKZI/t82YtnjlQk6vTshqo+XzaAoYWQBCDhlpZVKhwyvWtqUZ8ve/aYfBXlpuupMna5ATg2hCQACbd0XYVOnjhM+Zmpfb7sUMh07vRiLdtQpZa2aJ8vH8DQQUgCkFDb99frrcpDWjyj73e1tVs8Y6Tqmtv0ytbquK0DwODX81NuA8AxWFpWKUlxDUkLJ49QejikpWUVOmPKiLitZ6i4c/mOo97+/lPGJ6gSILEISQASaum6Ck0dmaPxw7Pito7MtBSdOWWElpZV6FuXzOyTwSqB/qa78Ipjx+42AAlT09CqV7ZVx7UVqd3iGSO160CjNlTUxX1dAAanbkOSmf3JzCrNbE0iCgIweD27sVKRqNO5CQhJ58zwRvJ+2t+9BwA9FUtL0i2SLoxzHQCGgKVllRqRk6a54wrivq7i3AzNGVfA6NsAeq3bkOScWyaJQ0QAHJPWSFTPbqjUOdOLlRJKTB+h82YU6/WdB1VZ15SQ9QEYXPqsT5KZXWtmK8xsRVUVI90CONKrW6tV19SWkF1t7dpH9GaXG4De6LOQ5Jy7yTm3wDm3oKio70fRBTCwLS2rVFo4pDMTeEj+tJG5Gj8sS0+sLU/YOgEMHhzdBiDunHN6qqxcZ0weoay0xI08Yma6YNZIvbRpv+qaWhO2XgCDA+MkAYi7tyoPaWd1oz75ruMSvu4LZpXo5ue36tkNVbpkzuiErx/orTuX71BbNKrK2mbtPtCoXQcbVdPYopz0sHIzUpWXmaqinHRNKspWiLHA4qLbkGRmd0laJGmEme2S9C3n3B/jXRiAwWOpf7LZc6cnrj9Su3njCzUiJ01PrC0nJGFAiEadXty8T3cs364N5XVqizpJUkZqSMOy01RR26y6plb5kzUqP0PnTC/WjFF5hKU+1m1Ics69LxGFABi8lq6r0Oyx+SrJz0j4ulNCpvNmjtTDq/equS2i9HBKwmsAYlHT0Kq7Xt2hu17Zoe37G5SVlqKTJg7ThGFZGluYpcKs1MOjx0edU0NLRG9V1OmZ9ZW6Y/kOjcrP0HkzRmr6qLwkP5LBg91tAOJq36Fmrdp5UNefOzVpNZw/q0R3vbJTL23ar7OnFyetDqBdx1OKNLdF9NLm/Xr+rSo1tUZVOjxb/7pgnGaNzlM4JbjrcMhMOelhzRtfqNljC/TGroN6Zn2lbnt5uy6cVaKzpnIAVV8gJAGIq2fWV8o5afHM5IWT048brpz0sJ5YW05IQr/RGolq+dZqPbehUvUtEc0oydXimSM1Kj+zR8tJCdnhsHTvyp16fG25mtuiWjyjmPMWHiNCEoC4WrquQqPzMzQzibsA0sMpWjStSE+tq9B3r3AJG8xyIOAkqYnnnNOa3TX6+5q9OtDQquOKsnXezBKNH3ZsJ31OCZn+ZcE4paaE9I8NlWppi2jJCaMISseAkAQgbppaI3r+rX26av7YpH9QXzCrRI+8sVev7Tigk0qHJbUWDF1r99Tovx9Zp5e3VKs4N10fWViqKcW5fbb8kJmumDdGaSkhvbh5v1ojTpfNHZ30999ARUgCEDfPbaxSY2tE581M/FFtnS2aVqS0lJCeWFNOSELC7TvUrJ88uUF/eXWnCjJTdemc0TqpdFhcWjVDZrp49iilppiWvbVPowoydMrE4X2+nqGAwSQBxM3Dq/doWHaaTj8u+R/QuRmpWrAKDgQAABsLSURBVDh5uJ5YVy7nXLLLwRDR0hbVzcu26OwfPat7V+zSR06fqGe/dLZOnTQ8rrt9zUznzyrRlOIcPfrGXlXUcv7C3iAkAYiLhpY2PV1WqSUnlHR5hE6inT+rRDurG7Vub22yS8Eg55zT42vKdcHPl+m7j5VpfmmhHr/+LH3zkpnKz0pNSA0hM101f6zSwyHd/epOtUaiCVnvYMLuNgBxsbSsUo2tEV0yu/8M4Hj+zJH6xgNr9MCq3Zo1Oj/Z5SAJYumo/v5Txh/TOlZsq9YX7lmtHdUNKspJ14dOK9W0kly9srVar2ytPqZl91RuRqqumj9Ot/5zm/6+plyXMqBqjxCSAMTFw6v3qCQvo1/1/xmek67FM0bqb6/t1g0XTldqP2nhwuBQtrdWP31qo55aV6HcjLCumDtGJ04oTPrRlNNKcrXwuOF6cfN+TSnO0QwGm4wZIQlAn6tpbNVzG6r0wdMmKNTPDre/esFYPb62XM+sr9QFs0qSXU6/E3WOU1v00Gs7Dui3/9ikpWWVykkP60vnT1VOeqrSwv0nhF8wq0Rb9tXrvtd26QuLpyorna//WPAsAehzT64tV0sk2i/PlfauqUUqyk3XvSt2DfmQVNPYqhXbqlVR26TKumZV1jWrtrH18IlTh+ekaWReho4fk68cvlSP0BaJ6pn1lbrlpW16afN+FWSl6vOLp+pDp09QQVZavxt/KpwS0tXzx+nX/3hLT5VV6LK5Y5Jd0oDAqx5An3to9R6NH5al2WP7X7+fcEpIV544Vjc/v0WVdU0qzk38+eSSrbymSX98YYvuXL5D9S0RpaaYinLTNXFEtgoyU3WwsVX7DjVr9a6DamqN6tE39+qEMfk6ddJwjSvMHNJj7mzfX6+7X92pv67cpcq6Zo3MS9d/Lpmh958yXtn9PEiW5HtDAby8Zb9OKh2m0QU9G9l7KOrfWxTAgLPvULNe2rxfn3zXpH77ZXr1grH6/XObdf9ru3Xdu45LdjkJU1nbpB89sUEPvL5bUSddPHuUxhZkaVRBRuAuNuecKuuatXxrtVbtOKDXdx7UmIJMXTCrRJOLc5LwCBLPOae1e2r1dFmlnl5foTd21Shk0tnTivXek8fr7GlF/ebozVgsnjFSq3cd1MNv7NG1Z/bf92h/QUgC0Kf+vqZckajrl7va2h1XlKP5Ewp178pduvaswf1F0b7bZ83uGt2/ardaI1EtKB2mMyePUGF22lHva2YamZehS+eM1gWzRur1nQe1bGOV/vTiVh0/Ok8XnTBKhVlHX8ZAE4l6pwx5bccBvbb9gJZvrdbemiaZSfPGFeiGC6fJZMrPTFVVXbPuWbEr2SX3SGZaii6YWaL7X9+t1btqNHdcQbJL6tcISQD61MOr92hKcY6mjey7Uy3Ew78sGKuv3PemVu08qBPHFya7nLhpao3o4dV7tGrnQY0tzNTV88epKDe9x8tJD6folInDdeL4Qj3/1j49t7FSGyrqdNbUIl05f4zSwylxqD6+mlsjKq9tUnltk/bWNKncv7T44wkV56brpNJhWjStSGdPL9aIHO9562/9jXpqfmmhXtlWrcfX7NWMUf37fZpshCQAfWbXgQa9uq1an188td+3zrx79mjd+NA63bti56ANSat3HtQvn35LtU2tOmd6sc6eVnzMh6OnpoR0zvRinTi+QH9fU+4NGPqL5/X9K2f3q+EeOjvY0KJVOw/q6fUVKq/xQlF1fcvh2zNSQyrJy9T80kK97+TxOnF8gcYUDM7+VyEzXTJ7lH6/bIue3VCljyycmOyS+i1CEoA+c8uL25Tij/Lb3+Wkh7XkhFF6ePVeffPiWcpMG3gtIUfzyBt79MV7VisrLUXXnXWcxh3jGeY7K8hK0/tOHq/5FXVaWlahq3//T73/lPH66kXTlZeRmBGlj6aitknPv7VPL23ep9d3HNSWffWSJJM0LDtNo/MzdOL4Qo3Kz9Co/AzlZ6YeDkRDYcDF8cOzNW9cgV7YtE87qxv6/PUxWBCSAPSJuqZW/eXVnXr37FED5qiZfz1pnO57bZfuXblTHzytNNnl9AnnnH71zCb99KmNWjChUOfPKonr4ftTR+bqhgun6adPbtSfXtyqpesq9NWLpuvyuWP6fIyso+3mijqnbfvq5SQ9/1aVNlYckiQNz07TiRMKddWCsZo3rlDry2sH5K7BeDh/VonW7KnRD5/YoF+9b16yy+mXCEkA+sTdr+7UoeY2ffyMSckuJWYnlRbq5NJh+tUzm3TV/LHKShvYH4lNrRF95b439ODre/SeeWP0P1eeoPtW7o77erPSwvr6xTN16dzR+voDa/SFe1br1pe26ZuXzNT8CfHbBdcWjWpLVb3W7K5R2d5a1bdElBYO6ZSJw3TV/LE6Y3KRppfkHhHWtvotSpDyM1N1xuQRenj1Hn3sjIl04g4wsD8RAPQLbZGo/vziNp0ycZhO6IdjI3XFzHTDhdN01e//qVte2qZPL5qc7JJ6raquWdfevkKrdhzUly+Ypk8vOi7h/Wlmjy3QA59eqPtX7dYPHl+vK3/3T108e5SuO+u4PntdtLRF9VZlndbuqdX68lo1tUaVHg5pWkmuZo3O1zcunjHgw24inTWlSG/urtH3Hi3T3dedOij7YB0LXkkAjtlja8q1+2Cjvn3prGSX0mMLSofpnOnF+v2zm3XNKROUn5n8/jQ9tb68Vh+7ZYX21zfrd9ecqItOGJW0WkIh05Xzx+rC40v0v89t1s3Pb9Ujb+zVnLH5uubUCbpk9uge9//asb9Bz22s1B3Ld2hz1SG1RpwyU1M0a1S+Zo3J03FFOYfPw0dA6pn01BR9bvFUfeOBNXpqXYXOH+Kj0HfGqwnAMXHO6Q/Pb9GkEdk6Z3pxssvplS+dP01Lfvm8blq2WV++YHqyy+mRp8sq9O93rVJORlj3Xnd6v2nJy04P6wvnT9PHz5qk+1/brdtf3q4b/vqG/uvhdZozLl+zxxZozth8TS/JO+IcZ42tEW2qPKSN5XXaWHlIb+46qG37GyR5Ha7nTyjUzFH5mjgiO24njh3oh/j31HtPGqc/v7hV3398vc6eXsyJnzsgJAE4Jq9srdYbu2r0ncuP73cns43VzNF5umTOaP3phW360OmlA+JUJc45/f65LfrhE+s1a3Se/vDBk1SS3//qzstI1YdOL9UHT5ug5Vur9dDqPVq986BuXrZFbVF31PuOLczU9JI8fWThRL1rapFe2rw/QVUPLakpIX31wum69vaV+surO/WBUycku6R+g5AE4Jjc/PxWFWal6soTk3/YfywtAO8/ZXzg9C+cN1WPvblXv/3HZt3Yz3cb1je36ct/Xa3H3izXu2eP0o+umt3vdzOZmU6dNFynThouyetkXra3VpsqDynq3g5LqSkhTSrK0ZTinHecC42QFD/nzRypkycO0y+WbtQV88ZwQmMfzwKAXntzV42eXl+hz5w9ecCPMzRxRLb+ZcE43bF8uz542gRNKuqf5ybbuq9e192+QpsqD+lrS6brEwP0/FsZqSmaN75Q8/yBPNsDbiQa1bo9tVq3p7ZXyx1qu8r6ipnpP5fM0GW/eVH/+9xmffH8ackuqV8gJAHolZa2qL7819UqykkfUIf9H831i6fo72v26trbV+r+T5+u3H4wKGI755weWr1HX39gjcIh020fPUVnTBmR7LIkHVsLHvqPOeMKdMmc0br5+S265pQJ/XL3baLROwtAr/z22U1aX16n715xgvKz+k+YOBYj8zL022tO1LZ99frcX15XpJs+M4my71CzPn3Ha/rcX17X5OIcPfSZM/pNQMLgcsMF0xSNSj99akOyS+kXCEkAeqxsb61+/cwmXTZ3tM6bOTLZ5fSp048boW9dOkvPrK/UD59Yn+xy9Pc39+qCny3T02WV+upF0/XXT57OKSQQN+OGZemDp03QvSt3qWxv73Z5DibsbgMGsO52c8RjF0drxNvNVpCVqm9d0r87OPfWB06doA3ltfrf57ZoanGurkzCueh++Ph6Pbm2QpuqDml0QYauOfU45WWk6u5Xdx6eh11YiIfPnDNZ96zYqf/5+3rd9tGTk11OUhGSAPTITcu2aM3uWv32mhM1LDstpvsMxM6037pkljZX1us//vamnKQrTxyTkA7Sm6sO6SdPbtBjb5YrKy1FS04YpdMmDY/bmEBAZwVZafrsOVP03cfK9PxbVTpzSlGyS0oaQhKAmD2zvkK/WPqWlpxQoiVJHNU5EVJTQvrtNSfq2ttX6Ev3rtbja/bqe1ecoOK8vu/M2hqJ6umySt2zYqee3VCpzNQUnTO9WGdMHqGM1IF91GC7gRiUh7IPnj5Bt/5zm777aJke+exwhYfoAJND81ED6LF7V+zUJ25bqWklufrO5Scku5yEKMxO01+uPU3fuHimnn9rn8772TI9sGq3on3Qobu5LaJ/bt6v7z1WptP+52l98v9Was3uGn1q0XF67oaztXjGyEETkDDwpIdT9LUlM7S+vE63vLQt2eUkDS1JGDA4zDg52kd2/sHj63XmlBH63b/NH1IDzaWETB87Y6IWTSvSl+5drevvfl3feXSdzp0+UufNHKkzpryztafzazXqnA7Ut6jqULMqapq0eV+9dh1oUFNrVCkh07nTi/WvJ43Tu6YWDdlf7Oh/Ljq+RItnFOvHT27Q+TNLNH740DtgYOh80gFD0LEGy/rmNv3oiQ265aVtunTOaP346jlHnGdrKDmuKEd//eTpeuzNvXpibbkee3Ov7l6xU2kpIRXnpWt4dpqG56SrIDNVGyvq1NwWVXNbVI2tEVXXtxwxnEBxbrrmjS/U5KIcTRyRrYzUFFXUNuueFbuS+AiBI5mZ/vvy43XeT5fpa/e/qds/dvKAHLj0WBCSgB4aCi1ae2sadctL23Tn8h2qa2rTRxdO1NffPWPAnputr6SETJfMGa1L5oxWS1tUy7fu1wub9qmqtln76ltUUdukDeV1ammLKj01pLRwSEXp6ZpekquinHQV5XqX/n4KEaDdqPxMfeWi6frGA2t032u7dVUSjvRMJt6pQD/Q1BpRTWOrDja0+tctOtjYqpr2/xtbDt9W29iq5raoIlGn6voWOUkpZgqnmFJTQkpNMWWmhZWdlqKstLCy09++zk4LKystReGUkELm3e9Qc5v2HGzUlqp6bdl3SGt21+jJtRWKOqeLThilj58x8fCpI/C2tHBIZ04pCjzyh07KGEyuOXm8Hly1W//9yDq9a2qRinLTk11SwhCSMKj0xbhBvfmCa4t4u1WaWqNqao3ouY1Vqm1sVW1Tq2ob2/zrVtU2tXWY3qq6pjbV+KGnKyHzDsnNz0xVfmaqCrLSlB4OKZxi2nWgUSEztUWd2iJRtUaiao041dQ0qaGlTY0tEXXXxfjbj6w74v/i3HR98LRSfWRh6ZActHAotBQCPREKmb5/5Qla8osXdOPDa/Xr980bMrvdCEkYMNoiUdU1tamuqdULJG1RtbRG1dQWUSTq5OR1kHVOh1tJUkKmUMi7TjFTRmpI4ZSQUkPmXfutL21Rp+bWiFoiUa3accALHVGnSCSqtqg7HH4aWyNqbouosSVyxLS2Tkc7/e65zUf8Hw6Z8jNTlZeZqryMsPIyUzU6P1N5mWHlZrSHH/86M00FWal6bkOVMtNSlBYOKdTLD6Soc2psiai+pU0NzRE1tLSpvsV7viJRp6hzmjOuQKPyMzRpRI5KR2T1q/OV4ehosUKiTC7O1b+fO1k/fnKjTp00XB84dUKyS0oIQhL6jUjUac/BRu2obtC2/fXasb9B2/c3aHt1g8prGnWgofWY1/G3Vbt7db/2gJWRmqLMtBRlpKYoPytNme3TUr1p3t8hXTp3tPIy2kNRqjJSQz3+5fXGrppe1dpRyEzZ6WFlp4el3OB5umsVoWXlnQgnGIo+tWiyXttxUN9+aK2mFufolEnDk11S3BGSkDCRqFNFbZP2HGzU7oON2nXAu959oFE7qxu080CDWiNvt8ikhUMaV5ipCcOzNX9CgcprmpWX4bW8ZKWlKD0cUnqqd50SMoXMZCaZpKjzWlHaW0wizikadXr37FGHd0m1RZxao1G1tkUVTgkpPex1tH1iTblS/JamsN8KFQ5Zj0LOhvJD3c4z1IIFgIEtJWT6+Xvn6vLfvKhP3/GaHvzMQo0tHNy75AlJiEkk6tTY6u2uaWrx+t8c/r81osaW6OG/G1q8TsgHGlpUXe9d9tY0qbym6R27pYZlp2lMQaZmjMrTBceXaMKwLE0Ynq0Jw7NUkpdxxNFUPfn1nmJSikydx+KbMDy72/uu2HYg5vUAwFCSl5Gqmz+4QJf/+kVde9tK3fep05WZNngHPSUkDQEtbVEdaPCOjvKuW3SgwTuS6qXN+7z+NW1ei0pzm9f5t6Utqhb/OhJ1aol03bE4SMik7LSwMtNSlJ0e1vwJhRpTkKkxhZkaU5CpsYWZGl2QyaHQgwS7n4Ch47iiHP3yffP00Vtf1ZfuXa1fvHfuoB0ElW+oAco5p7rmNlXVNauytlmVdU2qqmv2/j983aSd1Y1qbI10uZxwyPxdV14H4bRwSDnpYaVmh5SeElJqOKS0lJDSwl4H57RwyD/MPKS0FDt8++FpYa9TdFq46z44ew42ac/BJklHttiw+wkABoazpxfrPy6aru89tl7NbRH98n3zBuWP3sH3iAaJ5raI9h5s8vrs+P12dh9s1B7/Ul7bpKbWd7bupKWEVJSbruK8dJUOz1ZhVppyMsKHx8fJOnzt/Z2a0rO+NvGUiNaI/tTi0Z9qAYCeuvas45SZmqJvPbRW77t5uf70oQUanjO4xlAiJCXJnct3yDmnQ+2tQX7rT3sLUG1T2xHzm6TcjPDh8XLGFmYpNyOs3IywctJTlZsR7vVRVMBQQjgF+s4HTivVyLwMffauVbrydy/p1o+eHFPfz4HCnDv2s1l3tmDBArdixYo+X+5A0vmDuDUS9Y/s8o7uqqhtUmVd8xG7wtLCIRXnpqsoJ13Dc9IOj5dTkJWmvMywwqHBuc8XAJA8fdHVYeX2A/r4ra8q6qQvnj9V7z95/IDqp2RmK51zCzpPpyUpDhpbItqxv167a5oO7x6rqG1S+4FdGakhleRl6oQx+d6usdx0FedlKC8jTCsQAGDAmT+hUPd/eqH+429v6psPrtUdL+/QNy+ZqYWTRyS7tGMSU0gyswsl/UJSiqQ/OOe+H9eqBgjnnPbWNKlsb61/qVPZ3lpt3V+v9ga6rLQUjSnI1LQpRRpd4B3RVZiVShgCAAwqpSOydecnTtETayv03cfW6Zo/LNfZ04p09YJxOmd6sTI6j8kyAHQbkswsRdJvJJ0naZekV83sIefcuqPfc/Boboto14FGbd9fr237GrR9f702VNSpbG+dahrfHgV6/LAszRiVq0vnjta+uhaNKcykdQgAMGSYmS48vkSLphXpjy9s1Z9f3Kp/bKhSdlqKzps5UhfMKtEJY/M1piBzQHw3xtKSdLKkTc65LZJkZn+RdJmkpIYk55+jy/l/R53k5A634EQ73d4acf4JSNsvUTW3RtTU5g2EWNfUqoP+AIgH61tVdahZ5TVNqqht0v76liPWnZMe1uTiHC05YZRmjsrVjFF5mlaSe8Q5r+gcCgAYqjJSU/T/zp6s686apOVbq/Xw6j36+5pyPfD6HklSXkZYM0fnaUpxrtcXNzddI3LSVZiddvgI7Ez/KOyc9OT1DIplzWMk7ezw/y5Jp8SnnNhc9IvnVba3Ni7LTksJKT8rVSNy0lWSl6454wpUkpehsYWZKh3hjQY9PDttQCRgAACSKZwS0sLJI7Rw8gj912XH683dNSrbW6t1e2u1bk+tHnx99zuO5u4oLRzSxu9clMCKjxRLSApKA+84JM7MrpV0rf/vITPbcCyFDQIjJO1LdhGICdtq4GBbDRxsqwHimn6+rey7CVnNhKCJsYSkXZLGdfh/rKQ9nWdyzt0k6aZelTYImdmKoMMJ0f+wrQYOttXAwbYaONhWXYtlEINXJU0xs4lmlibpvZIeim9ZAAAAydVtS5Jzrs3MPiPpCXlDAPzJObc27pUBAAAkUUxdxp1zj0l6LM61DDbsehw42FYDB9tq4GBbDRxsqy7E5bQkAAAAA93AObEKAABAAhGS+oiZXW1ma80samZdHiVgZhea2QYz22RmX01kjfCY2TAze8rM3vKvC7uYL2Jmr/sXDlZIkO7eI2aWbmZ3+7cvN7PSxFcJKaZt9WEzq+rwPvp4MuqEZGZ/MrNKM1vTxe1mZr/0t+UbZnZiomvsjwhJfWeNpPdIWtbVDB1O8XKRpJmS3mdmMxNTHjr4qqSnnXNTJD3t/x+k0Tk3179cmrjyhq4Y3yMfk3TAOTdZ0s8k/SCxVULq0efZ3R3eR39IaJHo6BZJFx7l9oskTfEv10r6XQJq6vcISX3EOVfmnOtuAM3Dp3hxzrVIaj/FCxLrMkm3+n/fKunyJNaCI8XyHum4/f4q6VxjCPxk4PNsAHHOLZNUfZRZLpN0m/O8LKnAzEYlprr+i5CUWEGneBmTpFqGspHOub2S5F8XdzFfhpmtMLOXzYwglRixvEcOz+Oca5NUI2l4QqpDR7F+nl3p7775q5mNC7gd/QPfTwGSd9a4AcjMlkoqCbjpP51zD8ayiIBpHF4YB0fbVj1YzHjn3B4zmyTpGTN70zm3uW8qRBdieY/wPuofYtkOD0u6yznXbGaflNcCeE7cK0Nv8L4KQEjqAefc4mNcREyneMGxO9q2MrMKMxvlnNvrNydXdrGMPf71FjN7VtI8SYSk+IrlPdI+zy4zC0vK19F3IyA+ut1Wzrn9Hf69WfQf68/4fgrA7rbE4hQv/cNDkj7k//0hSe9oBTSzQjNL9/8eIWmhpHUJq3DoiuU90nH7XSXpGceAb8nQ7bbq1KflUkllCawPPfOQpA/6R7mdKqmmvVvCUEZLUh8xsysk/UpSkaRHzex159wFZjZa0h+cc0s4xUu/8X1J95jZxyTtkHS1JPlDN3zSOfdxSTMk/a+ZReX9mPi+c46QFGddvUfM7L8krXDOPSTpj5JuN7NN8lqQ3pu8ioeuGLfVv5vZpZLa5G2rDyet4CHOzO6StEjSCDPbJelbklIlyTn3e3ln1VgiaZOkBkkfSU6l/QsjbgMAAARgdxsAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAEAAQhIAAECA/w8yxCV0irW7yQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "data = wallstreet_import['compound_sentiment'].values\n",
    "\n",
    "ax = sns.distplot(data, bins=50)\n",
    "ax.set_title('Compound Sentiment Score Distribution', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rep_limit = 500\n",
    "excl_rep_outliers = wallstreet_v2[wallstreet_v2['authorReputation'] < rep_limit]\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(y='num_days_member_when_posted', x='authorReputation', data=excl_rep_outliers)\n",
    "plt.title('Correlation between Membership Duration and Reputation', fontsize=18)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_words_arr = joined_wordcloud_text.split(' ')\n",
    "word_freq_df = pd.DataFrame(pd.Series(all_words_arr).value_counts()).reset_index()\n",
    "word_freq_df.columns = ['word', 'freq'] \n",
    "\n",
    "top25_words = word_freq_df[:25]\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "ax = sns.barplot(x='word', y='freq', data=top25_words, palette='YlGnBu')\n",
    "ax.set_title('Word Frequency', fontsize=22)\n",
    "ax.set_xlabel('Words')\n",
    "ax.set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\morga\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_freq_dist = word_freq_df[word_freq_df['freq'] < 200]['freq']\n",
    "\n",
    "word_freq_dist.describe()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "ax = sns.distplot(word_freq_dist, bins=500)\n",
    "ax.set_title(\"Word Frequency Distribution\", fontsize=14);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pos_tag_arr = wallstreet_v2['parts_of_speech']\n",
    "# pos_dist = {}\n",
    "\n",
    "# for text in pos_tag_arr:\n",
    "#     for tag in text:\n",
    "#         if tag[1] not in pos_dist:\n",
    "#             pos_dist[tag[1]] = 1\n",
    "#         else:\n",
    "#             pos_dist[tag[1]] += 1\n",
    "\n",
    "# pos_freq_df = pd.DataFrame.from_dict(pos_dist, orient='index').reset_index()\n",
    "# pos_freq_df.columns = ['pos', 'freq']\n",
    "# pos_freq_df.sort_values(by=['freq'], axis=1)#, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "# ax = sns.barplot(x='pos', y='feq', data=pos_freq_df, palette='YlGnBu')\n",
    "# ax.set_title('Parts of Speech Frequency', fontsize=22)\n",
    "# ax.set_xlabel('Parts of Speech')\n",
    "# ax.set_ylabel('Frequency')\n",
    "\n",
    "# plt.tight_layout();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}